1. Field of the Invention
This invention relates to an apparatus for detecting an edge appearing in a picture, and more specifically to an edge detecting apparatus for detecting a white line on a road based on the image information of the road surface in order to implement automatic driving of a vehicle.
2. Description of the Related Art
Guiding information is necessary when a vehicle is automatically driven along a road. For this purpose, a white line on the road is an important information source. The vehicle can be automatically driven along the road with the guidance given by the white line, unless the white line has not been discontinued due to construction work or the like.
To this end, the road surface marked with a white line is first photographed with a video camera. Then, the white line may be recognized on the video screen by detecting an edge, that is, a sudden difference in the brightness or tint across the border of the normal road surface and white line.
Methods of recognizing an edge in an image remain a field which is rapidly developing as a part of an image analysis and processing technique applied to images from a satellite, etc. In this field, a variety of methods have been developed up to date (See, for example, A. Rosenfeld and A. Kak, "Digital Picture Processing", Academic Press, 1976).
Among the methods referred to above, there is known one called a SOBEL's operator. This is used to determine a primary differential value (gradient) indicative of variations in X- or Y-direction in the brightness or tint of a picture.
FIG. 11 shows a pair of SOBEL's operators. The operators are respectively of a mask M.sub.H for determining a gradient in a horizontal (X) direction and a mask M.sub.V for determining a gradient in a vertical (Y) direction. Each operator is provided with weighting elements in a 3.times.3 matrix. The intensity of the gradient in the horizontal or vertical direction is determined by performing a sum-of-products operation between respective weighting elements in each mask and the signal intensity J of pixels at corresponding positions of respective weighting elements in the mask. Then, the angle of a gradient vector with respect to the X-axis can be determined as follows: EQU Angle=tan.sup.-1 (J.times.M.sub.V /J.times.M.sub.H) (1)
The direction of an edge is normal to the direction of the so-determined gradient vector. However, this method is highly susceptible to noise because each mask is only 3.times.3 in size.
There is also known a template method. According to this method, a plurality of gradient detecting masks called templates, each having sensing directivity in a specific direction fixed to each, are provided. The sum-of-products operation with an image is performed on each template. Then, the direction in which a certain template has produced the maximum output among others is selected to be the direction of the gradient vector and the output value obtained by that template is taken as the intensity of the gradient vector. Similar template methods have also been proposed by ROBINSON, PREWITT, KIRSCH (See G. S. Robinson, "Edge Detection by Compass Gradient Masks", CGIP, Vol. 6, pp. 492-501 (1977); J. M. S. Prewitt, "Object Enhancement and Extraction," in Picture Processing and Psychopictories, B. S. Lipkin and A. Rosenfield (eds.), Academic Press, New York, 1970; and R. A. Kirsch, "Computer Determination of the Constituent Structure of Biological Images," Computers and Biomedical Research, Vol. 4, No. 3, pp. 315-328 (June 1979)).
An improvement over a simple operator in susceptibility to noise has been attained in the template methods referred to above due to the use of a plurality of templates each having the sensing directivity, however, these methods still have drawbacks that they tend to undergo the influence of noise as they employ 3.times.3 type templates.
There has also been proposed a method of detecting an edge in which a signal indicative of the brightness or tint of the original image is blurred by a GAUSSIAN function, and then, a secondary differential of the blurred image is calculated by .gradient..sup.2 G (where .gradient..sup.2 represents a Laplacian operator and G represents a GAUSSIAN function). This method attains an improvement in the susceptibility to noise but has a problem of a poor ability of detecting edges arranged at narrow intervals because each mask is big in size.
A HOUGH conversion method is generally used to determine a straight line from signals indicative of edges, which have been detected by use of the various techniques referred to above.
First of all, pixels arranged on a candidate straight line representative of the edge are extracted from the original image by use of the edge detecting operators. Then, each of pixels is given a set of coordinates (x.sub.i, y.sub.i) in an x-y plane, and all the loci .rho.=x.sub.i cos .alpha.+y.sub.i sin .alpha. each corresponding to each set of the coordinates (x.sub.i, y.sub.i) are counted up in a .rho.-.alpha. plane, where .rho. is the length of a perpendicular from the origin to the candidate straight line, and .alpha. is an angle between the perpendicular and X-axis. If a straight line represented by .rho..sub.0 =xcos.alpha..sub.0 +ysin.alpha..sub.0 exists in the given image, then all the loci in the .rho.-.alpha. plane representing the pixels on the straight line are duplicated at a point (.rho..sub.0, .alpha..sub.0) and a peak count should occur at that point. Thus, by detecting that peak count, the straight line representative of the edge can be determined.
Any straight line can be detected by the HOUGH conversion method in principle, even if the straight line is discontinued. However, in order for this method to work satisfactorily, the straight line should be sufficiently long and located sufficiently distant from others. In a case where a number of straight lines are arranged in parallel adjacent to each other at narrow intervals, this method tends to bring about erroneous or fake candidates, as illustrated in FIG. 12.